POINT CLOUD ENCODING METHOD AND DECODING METHOD, ENCODER AND DECODER AND STORAGE MEDIUM.

Abstract

The modalities of this application provide a point cloud encoding method and decoding method, an encoder and a decoder, and a storage medium.The point cloud encoding method comprises: when an encoder encodes geometric information on the basis of an octal tree, selecting n adjacent nodes from among all encoded adjacent nodes corresponding to a current node, n being an integer greater than or equal to 1 and less than or equal to 7; acquiring occupation bitmaps of the n adjacent nodes and an occupation bitmap of the current node, the occupation bitmaps being used to indicate whether a node comprises at least one point in a point cloud; determining a context according to the occupation bitmaps of the n adjacent nodes; and using the context to encode the occupation bitmap of the current node, and obtaining a code bitstream from the occupation bitmap of the current node.The point cloud decoding method comprises: when a decoder decodes geometric information on the basis of an octal tree, selecting n adjacent nodes from among all decoded adjacent nodes corresponding to a current node, n being an integer greater than or equal to 1 and less than or equal to 7; determining a context according to occupation bitmaps of the n adjacent nodes, the occupation bitmaps being used to indicate whether a node comprises at least one point in a point cloud; and using the context to parse the code bitstream of the current node, and obtain an occupation bitmap of the current node.

Description

Modalities of the present application relate to encoding and decoding technologies in the field of communications, and more particularly, to a point cloud encoding method and decoding method, an encoder, a decoder and a storage medium. BACKGROUND OF THE INVENTION In a point cloud exploration model (PCEM) encoder framework, an input point cloud can be divided into geometric information and attribute information corresponding to each point, where the geometric information of the point cloud and the attribute information corresponding to each point cloud are decoded separately. At present, in an octal tree-based geometric information encoding process, common geometric partitioning orders include a breadth-first traversal order and a depth-first traversal order. Whenever a node of an octal tree is partitioned, a node space occupancy bitmap contains eight flag bits (b0b4b2b3b4b5b6b7), which represent the occupancy situations of eight child nodes of the node, respectively. Occupancy information of the node's child nodes can be represented in encoding and decoding processes based on each flag bit in (b0b1b2b3b4b3b6b7). In an entropy coding process of an encoder and a parsing process of a decoder, each flag bit in (b0b1b2b3b4b5b6b7) can be encoded and decoded using contexts, where for each flag bit, a separate context corresponding to it can be used, and eight flag bits correspond to eight contexts. Because eight contexts are determined and maintained separately in the encoding and decoding processes, the spatial correlation between the node and its adjacent coded nodes is not fully utilized, thus decreasing the coding efficiency. BRIEF DESCRIPTION OF THE INVENTION Embodiments of the present application provide a point cloud encoding method and decoding method, an encoder, a decoder, and a storage medium, for fully utilizing the spatial correlation between a node and its coded adjacent nodes, thereby improving coding efficiency. Technical schemes of the modalities of this application can be implemented as follows. In a first aspect, embodiments of the present application provide a point cloud encoding method, which is implemented in an encoder and includes: selecting n adjacent nodes from among all encoded adjacent nodes corresponding to the current node when geometric information is encoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; acquiring occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determining contexts according to the occupancy bitmaps of the n adjacent nodes; and encoding the occupancy bitmap of the current node using the contexts to obtain the bitstream of the occupancy bitmap of the current node. In a second aspect, embodiments of the present application further provide a point cloud decoding method, which is implemented in a decoder and includes: selecting n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when geometric information is decoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; determining contexts according to occupancy bitmaps of the n adjacent nodes, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and parsing the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node. In a third aspect, embodiments of the present application further provide an encoder including a first selecting portion, an acquiring portion, a first determining portion, and an encoding portion, wherein the first selecting portion is configured to select n adjacent nodes from among all adjacent nodes corresponding to the current node when geometric information is encoded based on an octal tree, wherein n is an integer greater than or equal to 1 and less than or equal to 7; the acquiring portion is configured to acquire occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; The first determining portion is configured to determine contexts according to the occupancy bitmaps of the n adjacent nodes; and the encoding portion is configured to encode the occupancy bitmap of the current node using the contexts to obtain the bit stream of the occupancy bitmap of the current node. In a fourth aspect, embodiments of the present application further provide a decoder including a second selecting portion, a second determining portion, and a decoding portion, wherein the second selecting portion is configured to select n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when geometric information is decoded based on an octal tree, wherein n is an integer greater than or equal to 1 and less than or equal to 7; The second determining portion is configured to determine contexts according to occupancy bitmaps of the n adjacent nodes, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and the decoding portion is configured to parse the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node. In a fifth aspect, embodiments of the present application further provide an encoder, which includes a first processor, a first memory storing instructions executable by the first processor, a first communication interface, and a first bus used to connect to the first processor, the first memory, and the first communication interface, wherein the instructions, when executed by the first processor, implement the point cloud encoding method as described above. In a sixth aspect, embodiments of the present application further provide a decoder, which includes a second processor, a second memory storing instructions executable by the second processor, a second communication interface, and a second bus used to connect to the second processor, the second memory, and the second communication interface, wherein the instructions, when executed by the second processor, implement the point cloud decoding method as described above. In a seventh aspect, embodiments of the present application further provide a computer-readable storage medium having stored thereon a program implemented in an encoder, which, when executed by a processor, implements the point cloud encoding method as described above. In an eighth aspect, the modalities of the present application further provide to MA / t / ZUZZ / U í »441 computer-readable storage medium having stored thereon a program implemented in a decoder, which, when executed by a processor, implements the point cloud decoding method as described above. Embodiments of the present application provide a point cloud encoding method and decoding method, an encoder, a decoder, and a storage medium. The encoder selects n adjacent nodes from among all adjacent nodes corresponding to the current node when encoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; acquires occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determines contexts according to the occupancy bitmaps of the n adjacent nodes; and encodes the occupancy bitmap of the current node using the contexts to obtain the bit stream of the occupancy bitmap of the current node.The decoder selects n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when decoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; determines contexts according to occupancy bitmaps of the n adjacent nodes, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and parses the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node.It is then noted that in the embodiments of the present application, when the encoder or decoder encodes or decodes the occupancy bitmap of the current node in the point cloud, it may first determine contexts using the occupancy bitmaps of n adjacent nodes of the coded adjacent nodes of the current node, such that the obtained contexts make full use of the spatial correlation between the current node and the coded adjacent nodes. Therefore, when the occupancy bitmap of the current node is encoded or decoded according to the contexts, the coding efficiency can be effectively improved. BRIEF DESCRIPTION OF THE FIGURES FIGURE 1 is a schematic diagram of a PCEM encoding process; FIGURE 2 is a schematic diagram of a PCEM decoding process; FIGURE 3 is a schematic diagram of the splitting of a node in an octal tree; FIGURE 4 is a schematic diagram I of the application of a point cloud coding method; FIGURE 5 is a schematic diagram II of the application of a point cloud decoding method; FIGURE 6 is a schematic diagram I of an implementation process of a point cloud coding method; FIGURE 7 is a schematic diagram of a position relationship between the current node and its coded adjacent nodes; FIGURE 8 is a schematic diagram II of an implementation process of a point cloud coding method; FIGURE 9 is a schematic diagram III of an implementation process of a point cloud coding method; FIGURE 10 is a schematic diagram I of an implementation process of a point cloud decoding method; FIGURE 11 is a schematic diagram of a position relationship between the current node and its decoded adjacent nodes; FIGURE 12 is a schematic diagram II of an implementation process of a point cloud decoding method; FIGURE 13 is a schematic diagram I of a composition structure of an encoder; FIGURE 14 is a schematic diagram II of a composition structure of an encoder; FIGURE 15 is a schematic diagram I of a composition structure of a decoder; and FIGURE 16 is a schematic diagram II of a composition structure of a decoder. DETAILED DESCRIPTION OF THE INVENTION In order to understand the features and technical content of embodiments of the present application in more detail, implementations of the embodiments of the present application will be set forth in detail below in conjunction with the accompanying drawings, which are for reference only and are not intended to limit the embodiments of the present application. FIGURE 1 is a schematic diagram of a PCEM encoding process. As shown in FIGURE 1, the coordinate transformation of geometric information is performed in a geometric encoding process, such that the entire point cloud is contained within a bounding box and then quantized. Quantization in this step primarily plays a role in scaling. Due to the rounding of quantization, the geometric information of a portion of the point cloud is the same, so whether replicated points are removed is determined based on a parameter. The process of quantizing and removing replicated points is also referred to as a voxelization process. Then, octal tree splitting of the bounding box is performed.In an octal tree-based geometric information coding framework, the bounding box is equally divided into eight sub-cubes, and a non-empty sub-cube (including points in the point cloud) continues to be divided into eight equal parts until the leaf nodes obtained through the division become 1 x 1 x 1 unit cubes, and entropy coding of points is performed on the leaf nodes to generate a binary stream of a geometric slice. At present, the geometric splitting orders include a breadth-first traversal order and a depth-first traversal order. Specifically, the breadth-first traversal order means that when an octal tree is geometrically split, nodes at the current level will be split first, nodes at the next level will continue to be split until all nodes at the current level are split, and finally the split will stop when the leaf nodes obtained through the split become 1x1x1 unit cubes; the depth-first traversal order means that when the octal tree is geometrically split, a first node at the current level will be continuously split, and the splitting of the current node will not stop until the leaf nodes obtained through the split become 1x1x1 unit cubes.Nodes subsequent to the current level are split according to this order until all nodes at the current level are split. After geometric encoding is complete, the geometric information is reconstructed to guide attribute encoding. Currently, attribute encoding is primarily performed on color information. The color information (i.e., attribute information) is converted from an RGB (Red-Green-Blue) color space to a YUV (Luminance-Chrominance) color space. The point cloud is then recolored using the reconstructed geometric information, such that the unencoded attribute information corresponds to the reconstructed geometric information. When performing attribute prediction, the point clouds are first reordered based on Morton codes to generate a point cloud order that can be used for attribute prediction of the point clouds, and then attribute information prediction is performed using a differential method to obtain attribute prediction residuals, so that the attribute prediction residuals can be further quantized and coded and can be input into an entropy coding engine to obtain a stream. That is, in color information coding, after the point clouds are classified according to Morton codes, differential prediction of a consequent is directly performed, and finally the prediction residuals are quantized and coded to generate a binary stream of a portion. ΜΛ / t / ZUZZ / U í »441 attribute. FIG. 2 is a schematic diagram of a PCEM decoding process. As shown in FIG. 2, a geometric bit stream and an attribute bit stream in the acquired binary stream are decoded independently, respectively. When the geometric bit stream is decoded, geometric information of a point cloud is obtained through entropy decoding-octal tree reconstruction-inverse coordinate quantization-inverse coordinate translation; when the attribute bit stream is decoded, attribute information of the point cloud is obtained through entropy decoding-inverse quantization-attribute reconstruction-inverse space transformation, and a three-dimensional image model of the point cloud data to be encoded is restored based on the geometric information and the attribute information. It can be understood that the geometric information is decoded first, and then the attribute information is decoded in the decoding process. More specifically, a decoder first analyzes the binary stream of the geometric portion to obtain a geometric occupancy bitmap; the decoder reconstructs the octal tree according to the geometric occupancy bitmap, and obtains a geometric position through inverse coordinate quantization and inverse coordinate translation. The decoder analyzes the attribute stream to obtain quantized attribute prediction residuals and then obtains attribute prediction residuals after the inverse quantization process. Attribute reconstruction needs to be carried out using the reconstructed geometric information. Finally, the attribute information is obtained through an inverse space transformation. FIGURE 3 is a schematic diagram of node splitting of an octal tree. As shown in FIGURE 3, in a PCEM encoder framework, when the octal tree is geometrically split, Morton codes of a point cloud are calculated, and then the geometric octal tree is constructed from a root node N® (the 0th point at the 0th level, representing the xth point to the yth level) according to breadth-first Morton codes. Assuming that a geometric position of any point in the point cloud can be represented by a three-dimensional Cartesian coordinate (X, Y, Z). A value of each coordinate is represented by N bits, and a coordinate (Xk,Yk,Zk) of the k-th point can be represented by the following formulas: yk _ (vkvkvkvk} ¿d λ Λ —Λ0 J \U Yk= (Yw-iYw-2 -TIYo) (2) Zk= (z^_2...zfzok) (3) where X is a binary number represented by N binary bits x, that is, a binary number X, and x represents a value of a binary bit, which is either 0 or 1. A Morton code Mk corresponding to the k-th point can be represented by the following formula: λύk _ Avk -xik ^kvk -yjk ^k ^.k^.k^ky,k~7k^k\ (A\ m — ^N_1yN_1zN_1,xN_2yN_2zN_2f... x± y1, Xq i ¿0j Every three bits are represented as follows using an octal number m^: mk= (x„ykzk) ,n — 0,1,..., N — 1 (5) Then, formula (5) is substituted into formula (4), the Morton code Mkcorresponding to the k-th point can be represented as follows: Mk= (mN-imN-2 —mkmo) (6) The specific division process is as follows: 1. Each point is first assigned to one of eight child nodes according to the Morton code value m1^ (called the 0th octal number). Specifically, all points of which m1^ = 0 are assigned to the 0th child node, all points of which m1 / 7 = 1 are assigned to the 1st child node N}, and so on, and finally all points of which m1^ = 7 are assigned to the 7th child node N}. As described before, a node at the first level of the octal tree is composed of eight nodes. 2. Eight bits Β® = (b0b}b2b3b4b5b(1b7) indicate whether the eight child nodes of the root node Ng are occupied. If Nk(k = 0,1,... 7) contains at least one point in the point cloud, its corresponding bit is defined as bk= 1; if this child node does not contain any points, its corresponding bit is defined as bk=0. 3. The occupied node Nf at the first level is further divided into eight child nodes according to the Morton code value (called the first octal number) of each point; and occupancy information of its child nodes is represented by eight bits Bf, where ln is an index of the occupied node, n = 0,..., N1- 1, and N1 represents the number of occupied nodes at the first level. 4. The occupied node Ν[ at the t-th level (t=2, 3.....N-2) is further divided into eight child nodes according to the Morton code value (called the t-th octal number) of each point; and occupancy information of its child nodes is represented by eight bits B^, where ln is an index of the occupied node, n = 0, ...,ΛM -1, and represents the number of occupied nodes at the t-th level. 5. All nodes at the t-th level (t=N-1) become leaf nodes. If replicated points are allowed in an encoder configuration, the number of replicated points at the occupied leaf nodes must be encoded in a stream. MA / I It follows that whenever a node of the octal tree is split, a space occupancy bitmap of the node contains eight flag bits (Qb0b1b2b3b4b5b6b7'), which respectively represent occupancy situations of eight child nodes of the node. In the entropy coding process of the encoder and the parsing process of the decoder, a separate context is used for each flag bit in (bob^b^b^b^, that is, in the encoding or decoding process, eight contexts are separately determined and maintained, and the correlation between adjacent nodes is not utilized, that is, at present, when entropy coding is performed after the octal tree is geometrically split, the contexts are not determined according to occupancy bitmaps of adjacent nodes, and the spatial correlation between adjacent nodes is not effectively utilized, thereby decreasing the coding efficiency. In order to overcome the above drawbacks, the present application proposes a point cloud encoding method and a point cloud decoding method. When the encoder or decoder encodes or decodes the occupancy bitmap of the current node in the point cloud, it can first determine the contexts using the occupancy bitmaps of the n adjacent nodes of the coded adjacent nodes of the current node, such that the obtained contexts make full use of the spatial correlation between the current node and the coded adjacent nodes. Therefore, when the occupancy bitmap of the current node is encoded or decoded according to the contexts, the coding efficiency can be effectively improved. The point cloud encoding method and decoding method proposed by the present application may affect the entropy encoding process and the entropy decoding process in the point cloud encoder and decoder frames of the PCEM. Illustratively, FIG. 4 is a schematic application diagram of the point cloud encoding method. As shown in FIG. 4, the point cloud encoding method according to the present application may be applied at an entropy encoding position in the point cloud encoder frame of the PCEM. FIG. 5 is a schematic application diagram of the point cloud decoding method. As shown in FIG. 5, the point cloud decoding method according to the present application may be applied at an entropy decoding position in the point cloud decoder frame of the PCEM. The technical diagrams in the embodiments of this application will be described clearly and completely below in conjunction with the drawings in the embodiments of this application. An embodiment of the present application provides a cloud encoding method MA / I of points, which is implemented in an encoder. FIGURE 6 is a schematic diagram I of an implementation process of the point cloud encoding method. As shown in FIGURE 6, the steps of encoding an occupancy bitmap of the current node through the encoder may include steps 101-104. In step 101, n adjacent nodes are selected from all adjacent nodes corresponding to the current node when geometric information is encoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7. In the embodiment of the present invention, when the encoder encodes geometric information based on the octal tree, the encoder may select the n adjacent nodes of the current node. Specifically, the encoder may select the n adjacent nodes from among all encoded adjacent nodes corresponding to the current node to determine contexts. That is, in the present application, the n adjacent nodes of the current node are all coded nodes. Furthermore, in the embodiment of the present application, in an octal tree-based geometric information coding framework, a bounding box may be equally divided into 8 sub-cubes first, and an occupancy bitmap of each cube may be recorded, and then a non-empty sub-cube may be further divided into 8 equal parts until leaf nodes obtained through the division become 1x1x1 unit cubes. In this process, the encoder may predict the occupancy bitmap of the current node by utilizing the spatial correlation between the current node and its surrounding nodes, and then may perform entropy coding to generate a binary stream. Furthermore, in the embodiment of the present invention, when geometric information is encoded based on the octal tree, the encoder may first determine the encoding status of all adjacent nodes around the current node. Because the encoder performs octal tree splitting, there are 26 adjacent nodes around the current node, and among these 26 adjacent nodes, there are 7 encoded nodes, of which the encoding status is encoding completed. It should be noted that in the embodiment of the present application, the encryption status is used to determine whether an adjacent node has been encrypted, therefore the encryption status may be an encrypted or decrypted status. FIGURE 7 is a schematic diagram of a position relationship between the current node and its coded adjacent nodes. As shown in FIGURE 7, all adjacent nodes around the current node A include seven coded adjacent nodes, which are coded adjacent nodes 0, 1, 2, 3, 4, 5, and 6 respectively, where coded adjacent nodes 3, 5, and 6 are coded nodes that are coplanar with and MA / í I adjacent to the current node A, and the coded adjacent nodes 0, 1, 2, and 4 are coded nodes that are collinear with and adjacent to the current node A. It should be noted that in the embodiment of the present application, if the current node continues to be split, eight child nodes of the current node can also be obtained. Therefore, the current node and its corresponding 7 coded adjacent nodes can be viewed as 8 nodes obtained by splitting a node at the upper level. Furthermore, in the embodiment of the present application, when the encoder selects the n adjacent nodes from among all the encoded adjacent nodes corresponding to the current node, it may select any one or more of the encoded adjacent nodes or all of the encoded adjacent nodes. That is, in the present application, n can be an integer greater than or equal to 1 and less than or equal to 7. Illustratively, in the present application, the encoder may select three coded adjacent nodes, such as coded adjacent nodes 3, 5, 6 shown above in FIGURE 7, which are coplanar with and adjacent to the current node, from among all coded adjacent nodes of the current node. In step 102, occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node are acquired, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node. In the embodiment of the present application, after selecting n adjacent nodes from among all coded adjacent nodes corresponding to the current node, the encoder may acquire occupancy bitmaps corresponding to the n adjacent nodes, meanwhile, it may also acquire the occupancy bitmap of the current node. Specifically, a node corresponds to an occupancy bitmap, that is, the n adjacent nodes correspond to n occupancy bitmaps, and the current node corresponds to an occupancy bitmap. It should be noted that in the embodiment of the present application, an occupancy bitmap may be used to indicate whether a node is occupied or not. Specifically, an occupancy bitmap corresponding to a node may be used to indicate whether at least one point in the point cloud is contained within the node. Furthermore, in the embodiment of the present application, an occupancy bitmap of an adjacent node may indicate whether the adjacent node is occupied or unoccupied, that is, it may indicate that the adjacent node is empty or non-empty. Specifically, if the occupancy bitmap of the adjacent node indicates that the adjacent node is occupied, the corresponding adjacent node is non-empty, and accordingly, if the occupancy bitmap of the adjacent node indicates that the adjacent node is not occupied, the adjacent node is not empty. MA / The corresponding I is empty. It should be noted that in the embodiment of the present application, a value of an occupancy bitmap of a node may be 0 or 1. Specifically, the value of the occupancy bitmap of the occupied (non-empty) node may be 1, and the value of the occupancy bitmap of the unoccupied (empty) node may be 0. For illustrative purposes, if the current node's occupancy bitmap value is 1, this indicates that the current node is occupied and therefore not empty; if the current node's occupancy bitmap value is 0, this indicates that the current node is not occupied and therefore empty. It can be understood that in the embodiment of the present application, if eight child nodes of a node are occupied, this can be indicated by eight bits (b0b1b2b3b4b5b6b7'). If a child node corresponding to the node contains at least one point in the point cloud, a bit corresponding to the child node is defined as 1; if this child node does not contain any points in the point cloud, its corresponding bit is defined as 0. Illustratively, in the present application, the occupancy bitmaps bO, b1, b2, b3, b4, b5, b6, and b7 of eight nodes SO, S1, S2, S3, S4, S5, S6, and S7 in the same tier may be respectively used to indicate whether the nodes are occupied. For node S7, if its adjacent nodes SO, S1, S2, and S3 are occupied, then their corresponding occupancy bitmaps bO, b1, b2, and b3 are all 1, and if its adjacent nodes S4, S5, and S6 are not occupied, then their corresponding occupancy bitmaps b4, b5, and b6 are all 0. Furthermore, in the embodiment of the present application, if the encoder selects the n adjacent nodes from among all the coded adjacent nodes of the current node, the encoder may first read the occupancy bitmaps of the n adjacent nodes to obtain the n adjacent occupancy bitmaps. Illustratively, after the encoder selects three coded adjacent nodes 3, 5, and 6 which are coplanar with and adjacent to the current node A as shown in FIGURE 7, it may acquire three occupancy bitmaps corresponding to the coded adjacent nodes 3, 5, and 6, and may also determine the occupancy bitmap of the current node. In step 103, contexts are determined according to the occupancy bitmaps of the n adjacent nodes. In the embodiment of the present application, after acquiring the occupancy bitmaps of the n adjacent nodes and the occupancy bitmap of the current node, the encoder may determine contexts used to encode the occupancy bitmaps of the n adjacent nodes based on the occupancy bitmaps. It should be noted that in the embodiment of the present invention, whenever a node of an octal tree is partitioned, a space occupancy bitmap of the node may contain eight bits (b0b1b2b3b4b5b6b7), which represent occupancy situations of eight child nodes of the node, respectively. The encoder may perform entropy coding on each bit using a separate context. Specifically, a context-adaptive binary arithmetic coder (CABAC) is generally used to encode each bit (or bin) of the space occupancy bitmap to achieve a better compression effect. Furthermore, in the embodiment of the present application, a context value represents the probability that each character is either 1 or 0. Here, the encoder may establish a corresponding context for one or more input characters. The context, which represents a probability model of the input character, may be acquired from a set of existing models. Specifically, because a separate context is used for the occupancy bitmap of each node, that is, during the encoding or decoding process, the context corresponding to each node is determined and maintained separately, and the spatial correlation between the current node and its adjacent nodes will not be considered. In the embodiment of the present application, furthermore, FIGURE 8 is a schematic diagram II of an implementation process of a point cloud encoding method. As shown in FIGURE 8, the method of determining contexts by the encoder according to the occupancy bitmaps of the n adjacent nodes may include steps 103a and 103b. In step 103a, context indices are generated according to n. In the embodiment of the present application, the encoder may first generate the context indices according to the number n of the previously selected encoded adjacent nodes after acquiring n occupancy bitmaps of the n adjacent nodes. It can be understood that in the embodiment of the present application, when the encoder generates the context indices, it may execute a numbering process according to the number n of the previously selected encoded adjacent nodes, to obtain N context indices. Specifically, for the number n of the selected encoded adjacent nodes, the encoder may execute the numbering process using n bits as binary bits to obtain a numbering result, and then may pair the numbering result with decimal numbers to obtain N context indices, where N is a positive integer. Specifically, in the embodiment of the present application, the value of N is equal to 2n. Furthermore, in the embodiment of the present application, the context indices are decimal. Specifically, the N context indices may be 0, 1,..., 2n-1 sequentially. Illustratively, in the embodiment of the present application, the encoder selects MA / t / ZUZZ / U í I three adjacent coded nodes 3, 5 and 6 which are coplanes with and adjacent to the current node A shown in FIG. 7, where n=3, then the encoder can complete the numbering process by using three bits as binary bits according to the number 3 of the selected coded adjacent nodes to obtain a numbering result of (000, 001,010,011,100, 101, 110, 111), and match the numbering result with decimal numbers to obtain 8 context indices, which are 0,1,2, 3, 4, 5, 6 and 7 in sequence, that is, N=8. Illustratively, in the embodiment of the present application, if the encoder selects two coded adjacent nodes from among all coded adjacent nodes of the current node, i.e., n=2, then the encoder may execute numbering process using two bits as binary bits according to the quantity 2 of the selected coded adjacent nodes to obtain a numbering result of (00, 01, 10, 11), and pair the numbering result with decimal numbers to obtain four context indices, which are 0, 1, 2, 3 sequentially, i.e., N=4. It can be understood that in the embodiment of the present application, the encoder may select any n adjacent nodes for combination from all seven coded adjacent nodes of the current node, and may obtain N context indices according to the number n of the selected coded adjacent nodes after the numbering process is completed, wherein N is equal to 2n. In step 103b, the contexts are determined based on the occupancy bitmaps of the n adjacent nodes and the context indices. In the embodiment of the present application, the encoder may further determine the contexts based on the occupancy bitmaps of the n adjacent nodes and the context indices after generating the context indices according to n. It can be understood that in the embodiment of the present application, the encoder constructs contexts, essentially determining different contexts through different occupancy modes of adjacent nodes and matching the context modes with different context indices. Specifically, the encoder incorporates combinations of the occupancy modes of the encoded adjacent nodes of the current node using different contexts, and matches each of the occupancy modes to correspond to a context index. That is, in the embodiment of the present application, for the number n of the selected coded adjacent nodes, the encoder executes the numbering process using n bits as binary bits to obtain the numbering result, which includes combinations of all 2n occupancy modes composed of n occupancy bit maps of the n adjacent nodes, and establishes a corresponding relationship between the combinations of the 2n occupancy modes and decimal numbers, that is, a relationship MA / t / ZUZZ / U í I corresponding between contexts and context indices. Illustratively, in the embodiment of the present application, the encoder selects three coded adjacent nodes 3, 5 and 6 which are coplanar with and adjacent to the current node A as shown in FIG. 7, where n=3, then the encoder completes the numbering process using 3 bits as binary bits to obtain the numbering result of (000, 001, 010,011, 100, 101, 110, 111). Because the occupancy bitmaps of the coded adjacent nodes are either 0 or 1, the numbering result of (000, 001,010, 011,100,101,110,111) already includes combinations of all 8 occupancy modes composed of the occupancy bitmaps of the 3 nodes. The encoder can set 8 different contexts based on the 8 occupancy modes, and then can match the contexts with the decimal context indices.The context index corresponding to the context representing the occupancy mode of 000 is 0, the context index corresponding to the context representing the occupancy mode of 001 is 1, the context index corresponding to the context representing the occupancy mode of 010 is 2, the context representing the occupancy mode of 011 corresponds to the context index of 3, the context index corresponding to the context representing the occupancy mode of 100 is 4, the context index corresponding to the context representing the occupancy mode of 101 is 5, the context index corresponding to the context representing the occupancy mode of 110 is 6, and the context index corresponding to the context representing the occupancy mode of 111 is 7. Illustratively, in the embodiment of the present application, if the encoder selects two encoded adjacent nodes from among all the encoded adjacent nodes of the current node, i.e., n=2, then the encoder may execute the numbering process using 2 bits as binary bits to obtain the numbering result of (00, 01, 10, 11). Because the occupancy bitmaps of the encoded adjacent nodes are either 0 or 1, the numbering result of (00, 01, 010, 10, 11) already includes combinations of all the 4 occupancy modes composed of the occupancy bitmaps of the two nodes. The encoder may set 4 different contexts based on the 4 occupancy modes, and then may match the contexts with the decimal context indices.The context index corresponding to the context representing the occupancy mode of 00 is 0, the context index corresponding to the context representing the occupancy mode of 01 is 1, the context index corresponding to the context representing the occupancy mode of 10 is 2, and the context index corresponding to the context representing the occupancy mode of 11 is 3. That is, in the present application, if the numbers n of the encoded adjacent nodes selected by the encoder are different, the constructed contexts are different. MA / t / ZUZZ / U í »40 1 Specifically, for different numbers n of coded adjacent nodes, the combinations of occupancy modes of the occupancy bitmaps represented by the corresponding contexts are different even when the context indices are the same. For example, if n=3, the combination of occupancy modes of the represented occupancy bitmaps or the corresponding context is 001 when the context index is 1, and if n=2, the combination of occupancy modes of the occupancy bitmaps represented by the corresponding context is 01 when the context index is 1. It follows that in the present application, the contexts constructed by the encoder are associated with the n selected adjacent nodes, that is, the context corresponding to a node is no longer established independently, but is established using the encoded adjacent nodes with which the node has a spatial correlation. In the embodiment of the present application, furthermore, when the encoder determines the contexts based on the n adjacent occupancy bitmaps and the context indices, it may also construct m contexts corresponding to m context indices using the n adjacent occupancy bitmaps based on the m context indices of the N context indices. Specifically, in the present application, m is an integer greater than or equal to 1 and less than or equal to N. That is, in the present application, for the number n of encoded adjacent nodes, the encoder may set up at most N contexts to incorporate combinations of 2n occupancy modes. Optionally, the encoder may also set the number of contexts to m, i.e., the encoder may choose to build fewer than N contexts to incorporate combinations of a portion of the occupancy modes. Illustratively, in the embodiment of the present application, the encoder selects three coded adjacent nodes 3, 5, and 6 which are coplanar with and adjacent to the current node A as shown in FIG. 7 , where n=3, then the encoder completes the numbering process using 3 bits as binary bits to obtain the numbering result of (000, 001, 010, 011, 100, 101, 110, 111). Because the occupancy bitmaps of the coded adjacent nodes are either 0 or 1, the numbering result of (000, 001,010, 011,100,101,110,111) already includes combinations of all 8 occupancy modes composed of the occupancy bitmaps of the 3 nodes. The encoder can set 6 different contexts based on the 8 occupancy modes, i.e., m=6, and then can match the contexts with decimal context indices. The context index corresponding to the context representing the occupancy mode of 000 is 0, the context index corresponding to the context representing the occupancy mode of 000 is 1. MA / The context index corresponding to the context representing the occupancy mode of 001 is 1, the context index corresponding to the context representing the occupancy mode of 010 is 2, the context representing the occupancy mode of 011 corresponds to the context index of 3, the context index corresponding to the context representing the occupancy mode of 100 is 4, and the context index corresponding to the context representing the occupancy mode of 101 is 5. Illustratively, in the embodiment of the present application, if the encoder selects two encoded adjacent nodes from among all the encoded adjacent nodes of the current node, i.e., n=2, then the encoder may execute the numbering process using 2 bits as binary bits to obtain the numbering result of (00, 01, 10, 11). Because the occupancy bitmaps of the coded adjacent nodes are either 0 or 1, the numbering result of (00, 01, 010, 10, 11) already includes combinations of all the 4 occupancy modes composed of the occupancy bitmaps of the two nodes. The encoder may set 1 different context based on the 4 occupancy modes, i.e., m=1, and then may match the context with the decimal context index. The context index corresponding to the context representing the occupancy mode of 00 is 0. In step 104, the current node's occupancy bitmap is encoded using the context to obtain the bit stream of the current node's occupancy bitmap. In the embodiment of the present application, the encoder may encode the occupancy bitmap of the current node using the context after determining the contexts according to the occupancy bitmaps of the n adjacent nodes, to obtain the bit stream of the occupancy bitmap of the current node. Furthermore, in the embodiment of the present application, when the encoder encodes the occupancy bitmap of the current node using the contexts, it may select a target pattern from among all contexts determined based on the n occupancy bitmaps corresponding to the n adjacent nodes, and then may encode the occupancy bitmap of the current node according to the target pattern to finally obtain the corresponding bit stream, that is, the bit stream of the occupancy bitmap of the current node. In the embodiment of the present application, furthermore, FIG. 9 is a schematic diagram III of an implementation process of a point cloud encoding method. As shown in FIG. 9, the method of encoding the occupancy bitmap of the current node by the encoder using the contexts to obtain the bit stream of the occupancy bitmap of the current node may include steps 104a and 104b. In step 104a, the target model is determined from among the contexts according to the occupancy bitmaps of the n adjacent nodes. In the embodiment of the present application, when encoding the occupancy bitmap of the current node according to the contexts, the encoder may first select the target model using the occupancy bitmaps of the previously selected n adjacent nodes of the current node. It should be noted that in the embodiment of the present application, the encoder may first determine a context index corresponding to n occupancy bitmaps according to the n occupancy bitmaps of the n adjacent nodes, and then may determine a context corresponding to the context index as the target model. Illustratively, in the embodiment of the present application, the encoder selects three coded adjacent nodes 3, 5, 6 which are coplane with and adjacent to the current node A as shown in FIG. 7 and acquires three occupancy bitmaps corresponding to the coded adjacent nodes 3, 5, 6. If the occupancy bitmap of the coded adjacent node 3 is 1, the occupancy bitmap of the coded adjacent node 5 is 1, and the occupancy bitmap of the coded adjacent node 6 is 0, the encoder may determine that the corresponding context index is 6 based on the three occupancy bitmaps 1, 1, and 0, and therefore the encoder may determine the context with the context index of 6 as the target model. The target model may be used to differentiate the occupancy mode combination of 110. Illustratively, in the embodiment of the present application, if the encoder selects two encoded adjacent nodes from among all the encoded adjacent nodes of the current node and acquires two occupancy bitmaps corresponding to the two encoded adjacent nodes, which are 1 and 0 in sequence, then the encoder may determine that the corresponding context index is 2 based on the two occupancy bitmaps 1 and 0, and therefore the encoder may determine the context with the context index of 2 as the target model. The target model may be used to differentiate the combination of the occupancy mode of 10. It can be understood that in the present application, the contexts corresponding to the current node are associated with the n selected adjacent nodes, i.e., the context corresponding to a node is no longer established independently, but is established using the coded adjacent nodes with which the current node has a spatial correlation. Furthermore, the target model is selected from among the contexts based on the occupancy bitmaps of the coded adjacent nodes of the current node. In step 104b, binary arithmetic coding of the current node occupancy bitmap is executed using the target pattern to output the bit stream. In the embodiment of the present application, after determining the target pattern of the contexts according to the occupancy bitmaps of the n adjacent nodes, the encoder may further perform binary arithmetic encoding of the occupancy bitmap of the current node using the target pattern, and finally may output the binary bitstream of the occupancy bitmap of the current node. That is to say, in the application, only because the target model is selected from the contexts based on the occupancy bitmaps of the encoded adjacent nodes of the current node, when the encoder encodes the occupancy bitmap of the current node using the target model, the spatial relationship between the current node and the coded adjacent nodes can be fully utilized, thereby greatly improving the coding and decoding efficiency. It should be noted that in the embodiment of the present application, the value of the target model represents the probability that each character is 1 or 0. When the encoder encodes the occupancy bitmap of the current node using the target model, the probability that the occupancy bitmap is 1 or 0 is determined through the target model, that is, the target model may represent a probability model of the occupancy bitmap of the current node. Furthermore, in the embodiment of the present application, the target model is associated with the n occupancy bitmaps of the n adjacent nodes of the current node, and therefore when the probability that the occupancy bitmap of the current node is 0 or 1 is determined through the target model, binary arithmetic coding is performed on the basis of the occupancy bitmaps of the n adjacent nodes. It can be understood that in the embodiment of the present application, after the encoder encodes the occupancy bitmap of the current node using the contexts to obtain the bit stream of the occupancy bitmap of the current node, that is, after step 104 is executed, the method of executing the encoding through the encoder may further include step 105. In step 105, the target model is updated using the current node's occupancy bitmap. In the embodiment of the present application, the encoder can update the target model using the current node's occupancy bitmap. Specifically, the essence of the update is to update the probability that the target model represents 1 or 0. It should be noted that in the embodiment of the present invention, the encoder may determine the probability that the occupancy bitmap of the current node is 1 or 0 through the target model determined on the basis of the n adjacent nodes of all coded adjacent nodes of the current node. Therefore, the encoder may update the probability that the target model represents 1 or 0 using the occupancy bitmap. MA / t / ZUZZ / U í y4ól of the current node. Specifically, if the occupancy bitmap of the current node is 1, the probability that the target model represents 1 is increased; if the occupancy bitmap of the current node is 0, the probability that the target model represents 0 is increased. Illustratively, in the embodiment of the present application, the encoder selects three coded adjacent nodes 3, 5, and 6 which are coplanes with and adjacent to the current node A as shown in FIG. 7 , and acquires three occupancy bitmaps 1, 1, and 0 corresponding to the coded adjacent nodes 3, 5, and 6. After the encoder determines that the corresponding context index is 6 and determines the context with the context index of 6 as the target model, the encoder performs binary arithmetic coding of the occupancy bitmap of the current node using the target model, which differentiates the occupancy mode of 110, and outputs the binary current of the occupancy bitmap of the current node. Furthermore, the encoder may adjust the probability that the target model represents 1 and the probability that the target model represents 0 according to the occupancy bitmap of the current node.For example, if the current node's occupancy bitmap is 1, the probability that the target model represents 1 is increased and the probability that the target model represents 0 is decreased. Illustratively, in the embodiment of the present application, if the encoder selects two coded adjacent nodes from among all coded adjacent nodes of the current node and acquires two occupancy bitmaps corresponding to the two coded adjacent nodes, which are 1 and 0 sequentially, then after the encoder determines that the corresponding context index is 2 and determines the context with the context index 2 as the target model, the encoder performs binary arithmetic coding of the occupancy bitmap of the current node using the target model, which differentiates the occupancy mode from 10, and outputs the binary stream of the occupancy bitmap of the current node. Furthermore, the encoder may adjust the probability that the target model represents 1 and the probability that the target model represents 0 according to the occupancy bitmap of the current node.For example, if the current node's occupancy bitmap is 0, the probability that the target model represents 0 is increased and the probability that the target model represents 1 is decreased. It should be noted that in the embodiment of the present application, the encoder constructs the contexts based on the method composed of the above steps 101 to 105 using the n occupancy bitmaps of the n adjacent nodes of the current node, so that the spatial correlation between the current node and the encoded adjacent nodes can be fully utilized when the occupancy bitmap of the current node is encoded using the contexts. In the embodiment of the present application, furthermore, in the point cloud coding method proposed by the present application, the spatial correlation of the point cloud can be further utilized to cause an octal tree-based geometric information coding intra-prediction result to be more adaptable to entropy coding, thereby decreasing the code rate of the bit stream and achieving higher gain. Experimental results show that coding performance can be improved using the point cloud coding method proposed by the present application. Table 1 shows geometric information ratios under lossless compression in the point cloud coding method proposed by the present application.From Table 1, it can be seen that for different objectives, the code rate of geometric information is decreased and the code rate of attribute information remains unchanged under the same target quality using the point cloud coding method proposed by the present application, but the average code rate of geometric information and attribute information is also decreased accordingly. The smaller the code rate value, the greater the gain and the higher the performance; the higher the code rate, the lower the gain and the lower the performance. MA / t / ZUZZ / U í »44 1 Table 1 Sequence name Code rate of geometric information and attribute information (%) Code rate of geometric information (%) Code rate of attribute information (%) Basketball 93.5% 70.6% 100.0% Dancer 93.4% 69.3% 100.0% Sports 95.1% 75.0% 100.0% Model 93.5% 70.6% 100.0%The embodiment of the present application provides a point cloud encoding method. The encoder selects n adjacent nodes from among all adjacent nodes corresponding to the current node when encoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; acquires occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determines contexts according to the occupancy bitmaps of the n adjacent nodes; and encodes the occupancy bitmap of the current node using the context to obtain the bit stream of the occupancy bitmap of the current node.It is then noted that in the embodiments of the present application, when the encoder or decoder encodes or decodes the occupancy bitmap of the current node in the point cloud, it may first determine contexts using the occupancy bitmaps of n adjacent nodes of the coded adjacent nodes of the current node, such that the obtained contexts make full use of the spatial correlation between the current node and the coded adjacent nodes. Therefore, when the occupancy bitmap of the current node is encoded or decoded according to the context, the coding efficiency can be effectively improved. A further embodiment of the present application provides a point cloud decoding method, which is implemented in a decoder. FIG. 10 is a schematic diagram I of an implementation process of the point cloud decoding method. As shown in FIG. 10, steps for performing decoding in the actual code may include steps 201-203. In step 201, n adjacent nodes are selected from among all decoded adjacent nodes corresponding to the current node when geometric information is decoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7. In the embodiment of the present invention, when the decoder decodes geometric information based on the octal tree, the decoder may select the n adjacent nodes of the current node. Specifically, the decoder may select the n adjacent nodes from among all decoded adjacent nodes corresponding to the current node to determine contexts. That is, in the present application, the n adjacent nodes of the current node are all decoded nodes. Furthermore, in the embodiment of the present invention, in an octal tree-based geometric information decoding framework, the decoder first parses a binary stream of a geometric portion to obtain a geometric occupancy bitmap; the decoder reconstructs the octal tree according to the geometric occupancy bitmap, and obtains a geometric position through inverse coordinate quantization and inverse coordinate translation. Furthermore, in the embodiment of the present application, when geometric information is decoded based on the octal tree, the decoder may first determine the decoding status of all adjacent nodes around the current node. Because the decoder performs octal tree splitting, there are 26 adjacent nodes around the current node, and there are 7 encoded nodes, of which the decoding status is decoding complete, among these 26 adjacent nodes. It should be noted that in the embodiment of the present application, the decoding status is used to determine whether an adjacent node has been decoded, therefore the decoding status may be a decoded or non-decoded status. FIGURE 11 is a schematic diagram of a position relationship between the current node and its decoded adjacent nodes. As shown in FIGURE 11, all adjacent nodes around the current node B include seven decoded adjacent nodes, which are decoded adjacent nodes 0, 1, 2, 3, 4, 5, and 6 respectively, where the decoded adjacent nodes 3, 5, and 6 are decoded nodes that are coplanar with and adjacent to the current node B, and the decoded adjacent nodes 0, 1, 2, and 4 are decoded nodes that are collinear with and adjacent to the current node B. It should be noted that in the embodiment of the present application, the current node and its corresponding 7 decoded adjacent nodes can be viewed as 8 nodes obtained by splitting a node at the top level. Furthermore, in the embodiment of the present application, when the decoder selects the n adjacent nodes from among all the decoded adjacent nodes corresponding to the current node, it may select any one or more of the decoded adjacent nodes or all of the decoded adjacent nodes. That is, in the present application, n can be an integer greater than or equal to 1 and less than or equal to 7. Illustratively, in the present application, the decoder may select three decoded adjacent nodes, such as decoded adjacent nodes 3, 5, 6 shown above in FIGURE 11, which are coplanar with and adjacent to the current node, from among all decoded adjacent nodes of the current node. In step 202, the contexts are determined according to occupancy bitmaps of the n adjacent nodes, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node. In the embodiment of the present application, after selecting the n adjacent nodes from among all the decoded adjacent nodes corresponding to the current node, the decoder may first determine the contexts according to the occupancy bitmaps of the n adjacent nodes. Specifically, in the present application, a node corresponds to an occupancy bitmap, that is, the n adjacent nodes correspond to n occupancy bitmaps. It should be noted that in the embodiment of the present application, an occupancy bitmap may be used to indicate whether a node is occupied or not. Specifically, an occupancy bitmap corresponding to a node may be used to indicate whether at least one point in the point cloud is contained within the node. Furthermore, in the embodiment of the present application, an occupancy bitmap of an adjacent node may indicate whether the adjacent node is occupied or unoccupied, i.e., it may indicate that the adjacent node is not empty or is empty. Specifically, if the map MA / I adjacent node occupancy bitmap indicates that the adjacent node is occupied, the corresponding adjacent node is not empty, and consequently, if the adjacent node occupancy bitmap indicates that the adjacent node is unoccupied, the corresponding adjacent node is empty. It should be noted that in the embodiment of the present application, a value of an occupancy bitmap of a node may be 0 or 1. Specifically, the value of the occupancy bitmap of the occupied (non-empty) node may be 1, and the value of the occupancy bitmap of the unoccupied (empty) node may be 0. For illustrative purposes, if the value of the adjacent node's occupancy bitmap is 1, this indicates that the adjacent node is occupied and therefore not empty; if the value of the adjacent node's occupancy bitmap is 0, this indicates that the adjacent node is unoccupied and therefore empty. It can be understood that in the embodiment of the present application, if eight child nodes of a node are occupied, this can be indicated by eight bits (bab1b2b2h4bsbñb7). If a child node corresponding to the node contains at least one point in the point cloud, a bit corresponding to the child node is defined as 1; if this child node does not contain any points in the point cloud, its corresponding bit is defined as 0. Illustratively, in the present application, the occupancy bitmaps bO, b1, b2, b3, b4, b5, b6, and b7 of eight nodes SO, S1, S2, S3, S4, S5, S6, and S7 in the same tier may be respectively used to indicate whether the nodes are occupied. For node S7, if its adjacent nodes SO, S1, S2, and S3 are occupied, then their corresponding occupancy bitmaps bO, b1, b2, and b3 are all 1, and if its adjacent nodes S4, S5, and S6 are unoccupied, then their corresponding occupancy bitmaps b4, b5, and b6 are all 0. Furthermore, in the embodiment of the present application, if the decoder selects the n adjacent nodes from among all the decoded adjacent nodes of the current node, because the n adjacent nodes are decoded nodes, the decoder has obtained the occupancy bitmaps corresponding to the n adjacent occupancy bitmaps by analyzing the bit stream of the n adjacent nodes. FIGURE 12 is a schematic diagram II of an implementation process of a point cloud decoding method. As shown in FIGURE 12, before the decoder determines contexts according to the occupancy bitmaps of the n adjacent nodes, the method of executing decoding at the current node via the decoder may further include step 204. In step 204, the bit stream is parsed to obtain the occupancy bitmaps of the n adjacent nodes. In the embodiment of the present application, the decoder may first receive the bit stream, and then may parse the bit stream to obtain the occupancy bitmaps of the n adjacent nodes. It can be understood that in the embodiment of the present application, the decoder has completed decoding at 7 adjacent nodes of all adjacent nodes of the current node before executing decoding at the current node, therefore the number of all decoded adjacent nodes corresponding to the current node is 7. That is, before executing decoding at the current node, the decoder has obtained the occupancy bitmaps of the seven decoded adjacent nodes of the current node by analyzing the bit stream. Furthermore, in the embodiment of the present invention, the decoder obtains the occupancy bitmap of each of the decoded adjacent nodes of the current node by analyzing the bit stream. That is, the decoder obtains the occupancy bitmaps of the n adjacent nodes by analyzing the bit stream of the n adjacent nodes before determining contexts according to the occupancy bitmaps of the n adjacent nodes. Illustratively, the decoder selects three decoded adjacent nodes 3, 5, 6, which are coplanar with and adjacent to the current node B as shown in FIGURE 11, and determines contexts for decoding the occupancy bitmap of the current node based on the occupancy bitmaps of the n adjacent nodes after the occupancy bitmaps of the n adjacent nodes and the occupancy bitmap of the current node are acquired. It should be noted that, in the embodiment of the present invention, whenever a node of an octal tree is partitioned, a space occupancy bitmap of the node may contain eight bits (bob^b^b^b^), which represent occupancy states of eight child nodes of the node respectively. The decoder may perform entropy decoding of each bit using a separate context. Specifically, a context-adaptive binary arithmetic decoder (CABAC) is generally used to decode each bit (or bin) of the space occupancy bitmap to achieve a better compression effect. Furthermore, in the embodiment of the present application, a context value represents the probability that each character is either 1 or 0. At present, the decoder can establish a corresponding context for one or more input characters. The context, which represents a probability model of the input character, can be acquired from a set of existing models. Specifically, because a separate context is used for the occupancy bitmap of each node, that is, in the encoding or decoding process, the context MA / t / ZUZZ / U f I corresponding to each node is determined and maintained separately, and the spatial correlation between the current node and its adjacent nodes will not be considered. In the embodiment of the present application, furthermore, the method of determining the contexts by the decoder according to the occupancy bitmaps of the n adjacent nodes may include steps 202a and 202b. In step 202a, context indices are generated according to n. In the embodiment of the present application, the decoder may first generate the context indices according to the number n of the previously selected decoded adjacent nodes after acquiring n occupancy bitmaps of the n adjacent nodes. It can be understood that in the embodiment of the present application, when the decoder generates the context indices, it may execute a numbering process according to the number n of the previously selected decoded adjacent nodes, to obtain N context indices. Specifically, for the number n of the selected decoded adjacent nodes, the decoder may execute the numbering process using n bits as binary bits to obtain a numbering result, and then may pair the numbering result with decimal numbers to obtain N context indices, where N is a positive integer. Specifically, in the embodiment of the present application, the value of N is equal to 2n. Furthermore, in the embodiment of the present application, the context indices are decimal. Specifically, the N context indices may be 0, 1,..., 2n-1 sequentially. Illustratively, in the embodiment of the present application, the decoder selects three decoded adjacent nodes 3, 5, and 6 which are coplanar with and adjacent to the current node B shown in FIG. 11 , wherein n=3, then the decoder may complete the numbering process by using three bits as binary bits according to the quantity 3 of the selected decoded adjacent nodes to obtain a numbering result of (000, 001,010, 011, 100, 101, 110, 111), and match the numbering result with decimal numbers to obtain 8 context indices, which are 0, 1, 2, 3, 4, 5, 6, and 7 sequentially, that is, N=8. Illustratively, in the embodiment of the present application, if the decoder selects two decoded adjacent nodes from among all decoded adjacent nodes of the current node, i.e., n=2, then the decoder may execute numbering process using two bits as binary bits according to the quantity 2 of the selected decoded adjacent nodes to obtain a numbering result of (00, 01, 10, 11), and pair the numbering result with decimal numbers to obtain four context indices, which are 0, 1, 2, 3 sequentially, i.e., N=4. MA / t / ZUZZ / U / U4Ó I It can be understood that in the embodiment of the present application, the decoder may select any n adjacent nodes for combination from all seven decoded adjacent nodes of the current node, and may obtain N context indices according to the number n of the selected decoded adjacent nodes after the numbering process is completed, wherein N is equal to 2n. In step 202b, the contexts are determined based on the occupancy bitmaps of the n adjacent nodes and the context indices. In the embodiment of the present application, the decoder may further determine the contexts based on the occupancy bitmaps of the n adjacent nodes and the context indices after generating the context indices according to n. It can be understood that in the embodiment of the present application, the decoder constructs contexts, essentially determining different contexts through different occupancy modes of adjacent nodes and matching contexts with different context indices. Specifically, the decoder incorporates combinations of the occupancy modes of adjacent nodes decoded from the current node using different contexts, and matches each context with a context index. That is, in the embodiment of the present application, for the number n of the selected decoded adjacent nodes, the decoder executes the numbering process using n bits as binary bits to obtain the numbering result, which includes combinations of all 2n occupancy modes composed of n occupancy bit maps of the n adjacent nodes, and establishes a corresponding relationship between the combinations of the 2n occupancy modes and the decimal numbers, that is, a corresponding relationship between the contexts and the context indices. Illustratively, in the embodiment of the present application, the decoder selects three decoded adjacent nodes 3, 5 and 6 which are coplanar with and adjacent to the current node B as shown in FIG. 11 , where n=3, then the decoder completes the numbering process using 3 bits as binary bits to obtain the numbering result of (000, 001,010,011, 100, 101, 110, 111). Because the occupancy bitmaps of the decoded adjacent nodes are either 0 or 1, the numbering result of (000, 001, 010,011, 100, 101, 110, 111) already includes combinations of all 8 occupancy modes composed of the occupancy bitmaps of the 3 nodes. The decoder can set 8 different contexts based on the 8 occupancy modes, and then can match the contexts with the decimal context indices.The context index corresponding to the context representing the occupancy mode of 000 is 0, the context index corresponding to the context representing the occupancy mode of 001 is 1, the context index corresponding to the context representing the occupancy mode of 010 is 1. 2, the context representing the occupancy mode of 011 corresponds to the context index of 3, the context index corresponding to the context representing the occupancy mode of 100 is 4, the context index corresponding to the context representing the occupancy mode of 101 is 5, the context index corresponding to the context representing the occupancy mode of 110 is 6, and the context index corresponding to the context representing the occupancy mode of 111 is 7. Illustratively, in the embodiment of the present application, if the decoder selects two decoded adjacent nodes from among all decoded adjacent nodes of the current node, i.e., n=2, then the decoder may complete the numbering process using 2 bits as binary bits to obtain the numbering result of (00, 01, 10, 11). Because the occupancy bitmaps of the encoded adjacent nodes are either 0 or 1, the numbering result of (00, 01,010,10,11) already includes combinations of all 4 occupancy modes composed of the occupancy bitmaps of the two nodes. The decoder may establish 4 different contexts based on the 4 occupancy modes, and then may match the contexts with the decimal context indices.The context index corresponding to the context representing the occupancy mode of 00 is 0, the context index corresponding to the context representing the occupancy mode of 01 is 1, the context index corresponding to the context representing the occupancy mode of 10 is 2, and the context index corresponding to the context representing the occupancy mode of 11 is 3. That is, in the present application, if the numbers n of the decoded adjacent nodes selected by the decoder are different, the constructed contexts are different. Specifically, for different numbers n of decoded adjacent nodes, the combinations of the occupancy modes of the occupancy bitmaps represented by the corresponding contexts are different, even when the context indices are the same. For example, if n=3, the combination of the occupancy modes of the occupancy bitmaps represented by the corresponding context is 001 when the context index is 1, and if n=2, the combination of the occupancy modes of the occupancy bitmaps represented by the corresponding context is 01 when the context index is 1. It is then noted that in the present application, the contexts constructed by the decoder are associated with the n selected adjacent nodes, i.e. the context corresponding to a node is no longer established independently, but is established using the decoded adjacent nodes with which the node has a spatial correlation. In the embodiment of the present application, furthermore, when the decoder determines the ΜΛ / t / ZUZZ / U í »441 contexts based on the n adjacent occupancy bitmaps and the context indices, it can also construct m contexts corresponding to m context indices using the n occupancy bitmaps based on the m context indices of the N context indices. Specifically, in the present application, m is an integer greater than or equal to 1 and less than or equal to N. That is, in the present application, for the number n of adjacent nodes decoded, the decoder may establish at most N contexts to incorporate combinations of 2n occupancy modes. Optionally, the decoder may also determine the number of contexts as m, that is, the decoder may choose to construct fewer than N contexts to incorporate combinations of a portion of the occupancy modes. Illustratively, in the embodiment of the present application, the decoder selects three decoded adjacent nodes 3, 5 and 6 which are coplanar with and adjacent to the current node B as shown in FIG. 11 , where n=3, then the decoder executes the numbering process using 3 bits as binary bits to obtain the numbering result of (000, 001,010,011,100,101,110,111). Because the occupancy bitmaps of the decoded adjacent nodes are either 0 or 1, the numbering result of (000, 001,010,011, 100, 101, 110, 111) already includes combinations of all the 8 occupancy modes composed of the occupancy bitmaps of the 3 nodes. The decoder can set 6 different contexts based on the 8 occupancy modes, i.e. m=6, and then can match the contexts with the decimal context indices.The context index corresponding to the context representing the occupancy mode of 000 is 0, the context index corresponding to the context representing the occupancy mode of 001 is 1, the context index corresponding to the context representing the occupancy mode of 010 is 2, the context representing the occupancy mode of 011 corresponds to the context index of 3, the context index corresponding to the context representing the occupancy mode of 100 is 4, and the context index corresponding to the context representing the occupancy mode of 101 is 5. Illustratively, in the embodiment of the present application, if the decoder selects two decoded adjacent nodes from among all decoded adjacent nodes of the current node, i.e., n=2, then the decoder may execute the numbering process using 2 bits as binary bits to obtain the numbering result of (00, 01, 10, 11). Because the occupancy bitmaps of the encoded adjacent nodes are either 0 or 1, the numbering result of (00, 01,010, 10,11) already includes combinations of all 4 occupancy modes composed of the bitmaps of MA / í »40 1 occupancy of the two nodes. The decoder can establish 1 different context based on the 4 occupancy modes, i.e., m=1, and then it can match the context with the decimal context index. The context index corresponding to the context representing the occupancy mode of 00 is 0. In step 203, the bit stream of the current node is parsed using the context to obtain an occupancy bitmap of the current node. In the embodiment of the present application, after determining the contexts according to the occupancy bitmaps of the n adjacent nodes, the decoder may parse the bit stream of the current node using the contexts to obtain the occupancy bitmap of the current node. It can be understood that in the embodiment of the present application, the occupancy bitmap of the current node may indicate whether the current node is occupied. Specifically, the occupancy bitmap corresponding to the current node may be used to indicate whether at least one point in the point cloud is contained in the current node. It should be noted that in the embodiment of the present application, a value of the occupancy bitmap of the current node may be 0 or 1. Specifically, the value of the occupancy bitmap of the occupied (non-empty) node may be 1, and the value of the occupancy bitmap of the unoccupied (empty) node may be 0. For illustrative purposes, if the current node's occupancy bitmap value is 1, this indicates that the current node is occupied and therefore not empty; if the current node's occupancy bitmap value is 0, this indicates that the current node is unoccupied and therefore empty. Furthermore, in the embodiment of the present application, when the decoder analyzes the bit stream of the current node using the contexts, it may select a target model from among all contexts determined based on n occupancy bitmaps corresponding to the n adjacent nodes, and then may decode the bit stream of the current node according to the target model to finally obtain the bit stream of the occupancy bitmap of the current node. In the embodiment of the present application, furthermore, the method of parsing the bit stream of the current node by the decoder using the contexts to obtain the occupancy bitmap of the current node may include steps 203a and 204b. In step 203a, the target model is determined from among the contexts according to the occupancy bitmaps of the n adjacent nodes. In the embodiment of the present application, when decoding the occupancy bitmap of the current node according to the contexts, the decoder may first select the target model using the occupancy bitmaps of the n nodes. MA / t / ZUZZ / U í I previously selected adjacent nodes of the current node. It should be noted that in the embodiment of the present application, the decoder may first determine a context index corresponding to n occupancy bitmaps according to the n occupancy bitmaps of the n adjacent nodes, and then may determine a context corresponding to the context index as the target pattern. Illustratively, in the embodiment of the present application, the decoder selects three decoded adjacent nodes 3, 5, 6 which are coplane with and adjacent to the current node B as shown in FIG. 11 and acquires three occupancy bitmaps corresponding to the decoded adjacent nodes 3, 5, 6. If the occupancy bitmap of the decoded adjacent node 3 is 1, the occupancy bitmap of the decoded adjacent node 5 is 1, and the occupancy bitmap of the decoded adjacent node 6 is 0, the decoder may determine that the corresponding context index is 6 based on the three occupancy bitmaps 1, 1, and 0, and therefore the decoder may determine the context with the context index of 6 as the target model. The target model may be used to differentiate the occupancy mode combination of 110. Illustratively, in the embodiment of the present application, if the decoder selects two decoded adjacent nodes from among all decoded adjacent nodes of the current node and acquires two occupancy bitmaps corresponding to the two decoded adjacent nodes, which are 1 and 0 sequentially, then the decoder may determine that the corresponding context index is 2 based on the two occupancy bitmaps 1 and 0, and therefore the decoder may determine the context with the context index of 2 as the target model. The target model may be used to differentiate the combination of the occupancy mode of 10. It can be understood that in the present application, the contexts corresponding to the current node are associated with the n selected adjacent nodes, i.e., the context corresponding to a node is no longer established independently, but is established using the decoded adjacent nodes with which the current node has a spatial correlation. Furthermore, the target model is selected from among the contexts based on the occupancy bitmaps of the decoded adjacent nodes of the current node. In step 204b, the bit stream of the current node is parsed using the context to obtain the occupancy bitmap of the current node. In the embodiment of the present application, after determining the target model of the contexts according to the occupancy bitmaps of the n adjacent nodes, the decoder may further analyze the bitstream of the current node using the model. MA / í I objective to finally obtain the occupancy bitmap of the current node. That is to say, in the application, because the target model is selected from the contexts based on the occupancy bitmaps of the decoded adjacent nodes of the current node, when the decoder analyzes the bit stream of the current node using the context, the spatial relationship between the current node and the decoded adjacent nodes can be fully utilized, thereby greatly improving the encoding and decoding efficiency. It should be noted that in the embodiment of the present application, the value of the target model represents the probability that each character is 1 or 0. When the decoder analyzes the bit stream of the current node using the target model, the probability that the occupancy bitmap is 1 or 0 is determined through the target model, that is, the target model may represent a probability model of the occupancy bitmap of the current node. Furthermore, in the embodiment of the present application, the target model is associated with the n occupancy bitmaps of the n adjacent nodes of the current node, and therefore when the probability that the occupancy bitmap of the current node is 1 or 0 is determined using the target model, the decoding process is completed based on the occupancy bitmaps of the n adjacent nodes. It can be understood that in the embodiment of the present application, after the decoder parses the bit stream of the current node using the contexts to obtain the occupancy bitmap of the current node, that is, after step 203 is executed, the method of executing decoding through the decoder may further include step 205. In step 205, the target model is updated using the current node's occupancy bitmap. In the embodiment of the present application, the decoder can update the target model using the current node's occupancy bitmap. Specifically, the essence of the update is to adjust the probability that the target model represents 1 or 0. It should be noted that in the embodiment of the present application, the decoder may determine the probability that the occupancy bitmap of the current node is 1 or 0 through the target model determined on the basis of the n adjacent nodes of all decoded adjacent nodes of the current node. Therefore, the decoder may adjust the probability that the target model represents 1 or 0 using the occupancy bitmap of the current node. Specifically, if the occupancy bitmap of the current node is 1, the probability that the target model represents 1 is increased; if the occupancy bitmap of the current node is 0, the probability that the target model represents 1 is increased. MA / í I represents 0. Illustratively, in the embodiment of the present application, the decoder selects three decoded adjacent nodes 3, 5, and 6 which are coplanar with and adjacent to the current node B as shown in FIG. 11 , and acquires three occupancy bitmaps 1, 1, and 0 corresponding to the decoded adjacent nodes 3, 5, and 6. After the decoder determines that the corresponding context index is 6 and determines the context with the context index of 6 as the target model, the decoder executes decoding of the bit stream of the current node using the target model, and outputs the occupancy bitmap of the current node. Furthermore, the decoder may adjust the probability that the target model represents 1 and the probability that the target model represents 0 according to the occupancy bitmap of the current node.For example, if the current node's occupancy bitmap is 1, the probability that the target model represents 1 is increased and the probability that the target model represents 0 is decreased. Illustratively, in the embodiment of the present application, if the decoder selects two decoded adjacent nodes from among all decoded adjacent nodes of the current node and acquires two occupancy bitmaps corresponding to the two decoded adjacent nodes, which are 1 and 0 sequentially, then after the decoder determines that the corresponding context index is 2 and determines the context with the context index 2 as the target model, the decoder executes decoding of the bit stream of the current node using the target model, and outputs the occupancy bitmap of the current node. Furthermore, the decoder may adjust the probability that the target model represents 1 and the probability that the target model represents 0 according to the occupancy bitmap of the current node.For example, if the current node's occupancy bitmap is 0, the probability that the target model represents 0 is increased and the probability that the target model represents 1 is decreased. It should be noted that in the embodiment of the present application, the decoder constructs the contexts based on the method composed of the above steps 201 to 205 using the n occupancy bitmaps of the n adjacent nodes of the current node, so that the spatial correlation between the current node and the decoded adjacent nodes can be fully utilized when the bit stream of the current node is parsed using the contexts. The embodiment of the present application provides a point cloud decoding method. The decoder selects n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when decoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; determines contexts according to occupancy bitmaps of the n adjacent nodes, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and parses the bit stream of the current node using the context to obtain an occupancy bitmap of the current node.It is then noted that in the embodiments of the present application, when the encoder or decoder encodes or decodes the occupancy bitmap of the current node in the point cloud, it may first determine contexts using the occupancy bitmaps of n adjacent nodes of the encoded adjacent nodes of the current node, such that the obtained contexts make full use of the spatial correlation between the current node and the decoded adjacent nodes. Therefore, when the occupancy bitmap of the current node is encoded or decoded according to the context, the coding efficiency can be effectively improved. Based on the above embodiments, a further embodiment of the present application proposes an encoder. FIG. 13 is a schematic diagram I of a composition structure of the encoder. As shown in FIG. 13, the encoder 300 proposed by the embodiment of the present application may include a first selection portion 301, an acquisition portion 302, a first determination portion 303, an encoding portion 304, and an update portion 305. The first selection portion 301 is configured to select n adjacent nodes from among all adjacent nodes corresponding to the current node when geometric information is encoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7. The acquisition portion 302 is configured to acquire occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node. The first determination portion 303 is configured to determine contexts according to the occupancy bitmaps of the n adjacent nodes. The encoding portion 304 is configured to encode the occupancy bitmap of the current node using the contexts to obtain the bit stream of the occupancy bitmap of the current node. Furthermore, in one embodiment of the present application, the first determining portion 303 is specifically configured to generate context indices according to n; and determine the contexts based on the occupancy bitmaps of the n adjacent nodes and the context indices. Furthermore, in the embodiment of the present application, the first determining portion 303 is further specifically configured to execute a numbering process with MA / t / ZUZZ / U í U4Ó I base on n to get N context indices, where N is a positive integer. Furthermore, in the embodiment of the present application, the value of N is equal to 2n. Furthermore, in the embodiment of the present application, the first determining portion 303 is specifically configured to determine m contexts corresponding to m context indices using the occupancy bitmaps of the n adjacent nodes based on the m context indices of the N context indices, where M is an integer greater than or equal to 1 and less than or equal to N. Furthermore, in one embodiment of the present application, the encoding section 304 is specifically configured to determine a target pattern from among the contexts according to the occupancy bitmaps of the n adjacent nodes; and perform binary arithmetic encoding of the occupancy bitmap of the current node using the target pattern to output the bit stream. Furthermore, in the embodiment of the present application, the first update section 305 is configured to update the target model using the occupancy bitmap of the current node after the occupancy bitmap of the current node is encoded using the contexts to obtain the bit stream of the occupancy bitmap of the current node. FIGURE 14 is a schematic diagram II of a composition structure of the encoder. As shown in FIGURE 14, the encoder 300 proposed by the embodiment of the present application may include a first processor 306, a first memory 307 storing instructions executable by the first processor 306, a first communication interface 308, and a first bus 309 used to connect the first processor 306, the first memory 307, and the first communication interface 308. Furthermore, in the embodiment of the present application, the first processor 306 is used to select n adjacent nodes from among all adjacent nodes corresponding to the current node when encoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; acquire occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determine contexts according to the occupancy bitmaps of the n adjacent nodes; and encode the occupancy bitmap of the current node using the context to obtain the bit stream of the occupancy bitmap of the current node. Furthermore, several functional modules in the embodiment may be integrated into one processing unit, or several units may exist physically separate, or two or more than two units may be integrated into one unit. The integrated unit may be implemented in a hardware form, or it may be implemented in a form of a MA / í I functional software module. The integrated unit, if implemented in the form of a software functional module and not sold or used as a stand-alone product, may be stored on a computer-readable storage medium. Based on such an understanding, the technical schematics of the embodiments, in substance, or the portion contributing to the prior art, or all or a portion of the technical schematics, may be incorporated in the form of a software product, which is stored on a storage medium, and includes various instructions for causing a computing device (which may be a personal computer, a server, or a network device) or a processor to execute all or part of the steps of the methods according to the embodiments.The aforementioned storage medium includes various media, such as a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, which have the capacity to store program codes. The embodiment of the present application provides an encoder. The encoder selects n adjacent nodes from among all adjacent nodes corresponding to the current node when encoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; acquires occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determines contexts according to the occupancy bitmaps of the n adjacent nodes; and encodes the occupancy bitmap of the current node using the context to obtain the bit stream of the occupancy bitmap of the current node.It is then noted that in the embodiments of the present application, when the encoder or decoder encodes or decodes the occupancy bitmap of the current node in the point cloud, it may first determine contexts using the occupancy bitmaps of n adjacent nodes of the coded adjacent nodes of the current node, such that the obtained contexts make full use of the spatial correlation between the current node and the coded adjacent nodes. Therefore, when the occupancy bitmap of the current node is encoded or decoded according to the context, the coding efficiency can be effectively improved. Based on the above embodiments, in another embodiment of the present application, FIG. 15 is a schematic diagram I of a composition structure of a decoder. As shown in FIG. 15, the decoder 400 proposed by the embodiment of the present application may include a second selection portion 401, a second determination portion 402, a decoding portion 403, and a second MA / í I portion of update 404. The second selection portion 401 is configured to select n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when geometric information is decoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7. The second determination portion 402 is configured to determine contexts according to occupancy bitmaps of the n adjacent nodes, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node. The decoding portion 403 is configured to parse the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node. Furthermore, in the embodiment of the present application, the decoding section 403 is further configured to parse the bit stream to obtain the occupancy bitmaps of the n adjacent nodes before the contexts are determined according to the occupancy bitmaps of the n adjacent nodes. Furthermore, in the embodiment of the present application, the second determination portion 402 is specifically configured to generate context indices according to n; and determine the contexts based on the occupancy bitmaps of the n adjacent nodes and the context indices. Furthermore, in the embodiment of the present application, the second determining portion 402 is specifically configured to execute an n-based numbering process to obtain N context indices, where N is a positive integer. Furthermore, in the embodiment of the present application, the value of N is equal to 2n. Furthermore, in the embodiment of the present application, the second determining portion 402 is specifically configured to determine m contexts corresponding to m context indices using the occupancy bitmaps of the n adjacent nodes based on the m context indices of the N context indices, where M is an integer greater than or equal to 1 and less than or equal to N. Furthermore, in the embodiment of the present application, the decoding section 403 is specifically configured to determine a target model of the contexts according to the occupancy bitmaps of the n adjacent nodes; and analyze the bit stream of the current node using the target model to obtain the occupancy bitmap of the current node. Furthermore, in the embodiment of the present application, the second update section 404 is configured to update the target model using the occupancy bitmap of the current node after the bitstream of the current node is parsed using the MA / t / ZUZZ / U í »40 1 contexts to get the occupancy bitmap of the current node. FIGURE 16 is a schematic diagram II of a composition structure of the decoder. As shown in FIGURE 16, the decoder 400 proposed by the embodiment of the present application may further include a second processor 405, a second memory 406 storing instructions executable by the second processor 405, a second communication interface 407, and a second bus 408 used to connect the second processor 405, the second memory 406, and the second communication interface 407. Furthermore, in the embodiment of the present application, the second processor 405 is used to select n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when decoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; determine contexts according to occupancy bitmaps of the n adjacent nodes, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and analyze the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node. Furthermore, several functional modules in the embodiment may be integrated into a single processing unit, or several units may exist physically separate, or two or more units may be integrated into a single unit. The integrated unit may be implemented in hardware form, or it may be implemented as a software functional module. The integrated unit, if implemented in the form of a software functional module and not sold or used as a stand-alone product, may be stored on a computer-readable storage medium. Based on such an understanding, the technical schematics of the embodiments, in substance, or the portion contributing to the prior art, or all or a portion of the technical schematics, may be incorporated in the form of a software product, which is stored on a storage medium, and includes various instructions for causing a computing device (which may be a personal computer, a server, or a network device) or a processor to execute all or part of the steps of the methods according to the embodiments.The aforementioned storage medium includes various media, such as a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, which have the capacity to store program codes. The embodiment of the present application provides a decoder. The decoder selects n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when decoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; determines MA / t / ZUZZ / U í U4Ó I contexts according to occupancy bitmaps of the n adjacent nodes, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and parsing the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node. It is then had that in the embodiments of the present application, when the encoder or the decoder encodes or decodes the occupancy bitmap of the current node in the point cloud, it may first determine the contexts using the occupancy bitmaps of the n adjacent nodes of the coded adjacent nodes of the current node, such that the obtained contexts make full use of the spatial correlation between the current node and the coded adjacent nodes.Therefore, when the current node occupancy bitmap is encoded or decoded according to the contexts, the coding efficiency can be effectively improved. An embodiment of the present application provides computer-readable storage media having programs stored thereon, which, when executed by a processor, implement the methods described in the previous embodiments. Specifically, program instructions corresponding to a point cloud encoding method according to the embodiment may be stored in a storage medium such as an optical disk, a hard disk, a U disk, etc. When the program instructions in the storage medium corresponding to the point cloud encoding method are read or executed by an electronic device, the method includes the following steps: select n adjacent nodes from among all encoded adjacent nodes corresponding to the current node when geometric information is encoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; acquiring occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determining contexts according to the occupancy bitmaps of the n adjacent nodes; and encoding the occupancy bitmap of the current node using the contexts to obtain the bitstream of the occupancy bitmap of the current node. When the program instructions on the storage medium corresponding to a point cloud decoding method are read or executed by an electronic device, the method includes the following steps: select n adjacent nodes from all decoded adjacent nodes ΜΛ / t / ZUZZ / U í »441 corresponding to the current node when geometric information is decoded based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; determining contexts according to occupancy bitmaps of the n adjacent nodes, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and parsing the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node. One skilled in the art should understand that embodiments of the present application may be provided as methods, systems, or computer program products. Thus, the present application may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk memory, optical memory, etc.) containing computer-usable program code. The present application is described with reference to implementation flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams and combinations of flows and / or blocks in the flowcharts and / or block diagrams may be implemented by computer program instructions.These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processing machine, or other programmable data processing devices to generate a machine, such that the instructions being executed by the processor of the computer or other programmable data processing devices generate an apparatus for implementing functions specified in one or more flows in the implementation flowcharts and / or one or more blocks in the block diagrams. These computer program instructions may also be stored in a computer-readable memory that can instruct the computer or other programmable data processing devices to operate in a particular manner, such that the instructions stored in the computer-readable memory generate an article of manufacture including an instruction apparatus, wherein the instruction apparatus implements functions specified in one or more flows in the implementation flow charts and / or one or more blocks in the block diagrams. These computer program instructions can also be loaded into the MA / t / ZUZZ / U / U4Ó I computer or other programmable data processing devices to cause a series of operational steps to be executed on the computer or other programmable devices to generate computer-implemented processing, such that the instructions executed on the computer or other programmable devices provide steps to implement functions specified in one or more flows in the implementation flowcharts and / or one or more blocks in the block diagrams. What has been described are simply preferred embodiments of the present application and are not intended to limit the scope of protection of the present application. Industrial applicability Embodiments of the present application provide a point cloud encoding method and decoding method, an encoder, a decoder, and a storage medium. The encoder selects n adjacent nodes from among all adjacent nodes corresponding to the current node when encoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; acquires occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determines contexts according to the occupancy bitmaps of the n adjacent nodes; and encodes the occupancy bitmap of the current node using the contexts to obtain the bit stream of the occupancy bitmap of the current node.The decoder selects n adjacent nodes from among all decoded adjacent nodes corresponding to the current node when decoding geometric information based on an octal tree, where n is an integer greater than or equal to 1 and less than or equal to 7; determines contexts according to occupancy bitmaps of the n adjacent nodes, where an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and parses the bit stream of the current node using the contexts to obtain an occupancy bitmap of the current node.It is then noted that in the embodiments of the present application, when the encoder or decoder encodes or decodes the occupancy bitmap of the current node in the point cloud, it may first determine contexts using the occupancy bitmaps of n adjacent nodes of the coded adjacent nodes of the current node, such that the obtained contexts make full use of the spatial correlation between the current node and the coded adjacent nodes. Therefore, when the occupancy bitmap of the current node is encoded or decoded according to the contexts, the coding efficiency can be effectively improved.

Claims

1. A point cloud encoding method applied in an encoder, the method being characterized in that it comprises: selecting n adjacent nodes from the encoded adjacent nodes corresponding to a current node when geometric information is encoded on the basis of an octal tree, wherein n is an integer greater than or equal to 1 and less than or equal to 7; acquiring occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, wherein the occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; determining contexts according to the occupancy bitmaps of the n adjacent nodes; and encoding the occupancy bitmap of the current node using the contexts to obtain the bitstream of the occupancy bitmap of the current node.

2. The method according to claim 1, characterized in that the determination of the contexts according to the occupancy bitmaps of the n adjacent nodes comprises: generating context indices according to the n; and determining the contexts based on the occupancy bitmaps of the n adjacent nodes and the context indices.

3. The method according to claim 2, characterized in that the generation of the context indices according to n comprises: executing a numbering process based on n to obtain N context indices, wherein N is a positive integer.

4. The method according to claim 1, characterized in that n is equal to 3.

5. The method according to claim 4, characterized in that a value of N is equal to 2n.

6. The method according to claim 5, characterized in that the value of N is equal to 8.

7. The method according to claim 3, characterized in that the determination of the contexts based on the occupancy bit maps of the n adjacent nodes and the context indices comprises: determining m contexts corresponding to am context indices using the occupancy bit maps of the n adjacent nodes based on the m context indices of the N context indices, wherein M is an integer greater than or equal to 1 and less than or equal to N.

8. The method according to claim 1, characterized in that the encoding of the current node occupancy bitmap using the contexts to obtain the bitstream of the current node occupancy bitmap comprises: determining a target model of the contexts according to the occupancy bitmaps of the n adjacent nodes; and performing binary arithmetic encoding on the current node occupancy bitmap using the target model to output the bitstream.

9. The method according to claim 8, characterized in that after the current node occupancy bitmap is encoded using contexts to obtain the current node occupancy bitmap bitstream, the method further comprises: updating the target model using the current node occupancy bitmap.

10. A point cloud decoding method applied in a decoder, the method being characterized in that it comprises: selecting n adjacent nodes from among the decoded adjacent nodes corresponding to a current node when geometric information is decoded on an octal tree basis, wherein n is an integer greater than or equal to 1 and less than or equal to 7; determining contexts according to occupancy bitmaps of the n adjacent nodes, wherein the occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and analyzing the bitstream of the current node using the contexts to obtain an occupancy bitmap of the current node.

11. The method according to claim 10, characterized in that before the contexts are determined according to the occupancy bitmaps of the n adjacent nodes, the method further comprises: analyzing the bit stream to obtain the occupancy bitmaps of the n adjacent nodes.

12. The method according to claim 10, characterized in that the determination of the contexts according to the occupancy bitmaps of the n adjacent nodes comprises: generating context indices according to the n; and determining the contexts based on the occupancy bitmaps of the n adjacent nodes and the context indices.

13. The method according to claim 12, characterized in that the generation of context indices according to n comprises: executing a numbering process based on n to obtain N context indices, wherein N is a positive integer.

14. The method according to claim 10, characterized in that n is equal to 3.

15. The method according to claim 14, characterized in that a value of N is equal to 2n.

16. The method according to claim 15, characterized in that the value of N is equal to 8.

17. The method according to claim 13, characterized in that the determination of the contexts based on the occupancy bitmaps of the n adjacent nodes and the context indices comprises: determining m contexts corresponding to am context indices using the occupancy bitmaps of the n adjacent nodes based on the m context indices of the N context indices, wherein M is an integer greater than or equal to 1 and less than or equal to N.

18. The method according to claim 10, characterized in that the analysis of the bit stream of the current node using the contexts to obtain the occupancy bit map of the current node comprises: determining a target model from among the contexts according to the occupancy bit maps of the n adjacent nodes; and analyzing the bit stream of the current node using the target model to obtain the occupancy bit map of the current node.

19. The method according to claim 18, characterized in that after the bit stream of the current node is analyzed using the contexts to obtain the bitmap of the current node's occupation, the method further comprises: updating the target model using the bitmap of the current node's occupation.

20. An encoder characterized in that it comprises a first selection portion, an acquisition portion, a first determination portion, and an encoding portion, wherein the first selection portion is configured to select n adjacent nodes from among the encoded adjacent nodes corresponding to a current node when geometric information is encoded on the basis of an octal tree, wherein n is an integer greater than or equal to 1 and less than or equal to 7; the acquisition portion is configured to acquire occupancy bitmaps of the n adjacent nodes and an occupancy bitmap of the current node, wherein the occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; the first determination portion is configured to determine contexts according to the occupancy bitmaps of the n adjacent nodes;and the encoding portion is configured to encode the current node's occupancy bitmap using contexts to obtain the bitstream of the current node's occupancy bitmap.; 21. A decoder characterized in that it comprises a second selection portion, a second determination portion, and a decoding portion, wherein the second selection portion is configured to select n adjacent nodes from among the decoded adjacent nodes corresponding to a current node when geometric information is decoded on the basis of an octal tree, wherein n is an integer greater than or equal to 1 and less than or equal to 7; the second determination portion is configured to determine contexts according to occupancy bitmaps of the n adjacent nodes, wherein an occupancy bitmap is used to indicate whether at least one point in a point cloud is contained in a node; and the decoding portion is configured to analyze the bitstream of the current node using the contexts to obtain an occupancy bitmap of the current node.

22. An encoder characterized in that it comprises a first processor and a first memory that stores instructions executable by the first processor, wherein the instructions, when executed by the first processor, implement the method according to any one of claims 1 to 9.

23. A decoder characterized in that it comprises a second processor and a second memory that stores instructions executable by the second processor, wherein the instructions, when executed by the second processor, implement the method according to any of claims 10 to 19.

24. A computer-readable storage medium characterized in that it has stored therein a program applied in an encoder, which, when executed by a processor, implements the method according to any of claims 1 to 9.

25. A computer-readable storage medium characterized in that it has stored therein a program applied in a decoder, which, when executed by a processor, implements the method according to any of claims 10 to 19.

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